Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
An analyst is performing a join between a large fact table and a small dimension table. To ensure optimal performance in Databricks SQL, which join type is preferred when the dimension table fits in memory?
⚠ Common exam trap
Candidates often confuse shuffle hash joins or sort-merge joins with broadcast joins, overlooking the distinct performance advantage of broadcasting a small dimension table that fits in memory.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Broadcast hash join
When one side of a join is significantly smaller than the other, the engine can use a broadcast join. By sending the smaller table to all worker nodes, the engine avoids a costly shuffle of the larger fact table. This is the most efficient way to perform joins in a distributed environment, as it minimizes network traffic and accelerates data processing speeds significantly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Sort-merge join
Why it's wrong here
Sort-merge joins are typically chosen by the optimizer for large-to-large table joins. This operation requires sorting and shuffling both tables, which is computationally expensive and slow for this scenario. It should be avoided when one side is small enough to be broadcast efficiently across the distributed worker cluster.
- ✓
Broadcast hash join
Why this is correct
A broadcast join broadcasts the smaller table to every executor, allowing the join to occur locally at each node without shuffling the large fact table. This significantly reduces data movement across the network, which is the primary bottleneck in distributed join operations within Databricks SQL environments.
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Cartesian join
Why it's wrong here
Cartesian joins produce a cross-product of all rows, which is mathematically intensive and almost always unintentional. They lead to massive data explosion and will likely crash the query or cause severe performance degradation due to the sheer volume of output rows generated by the cross-join calculation.
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Full outer join
Why it's wrong here
A full outer join returns all records when there is a match in either the left or right table. It does not dictate the physical execution strategy for the join itself, such as broadcasting or shuffling. Relying on this join type does not inherently optimize performance for small-to-large table joins.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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